技术领域technical field
本发明具体涉及一种图像二值化分割方法。The invention specifically relates to an image binarization segmentation method.
背景技术Background technique
随着国家经济技术的发展和人们生活水平的提高，图像处理技术已经广泛应用于人们的生产和生活之中，给人们的生产和生活带来了无尽的便利。With the development of national economy and technology and the improvement of people's living standards, image processing technology has been widely used in people's production and life, bringing endless convenience to people's production and life.
图像分割方法是图像处理的重要手段。图像分割是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程，它是由图像处理到图像分析的关键步骤，其分割结果的正确性直接影响着目标检测和识别的智能化程度。图像分割方法经历数十年的发展，出现了大量不同类型的算法。目前图像分割法大致可分为：基于阈值的方法、基于区域的方法、基于边缘的方法、区域与边缘相结合的方法以及多尺度分割法等几大类。除此之外，神经网络、模糊数学、数学形态学等理论近年来也广泛应用于图像分割领域，涌现出众多新算法。这些算法原理虽各不相同，但基本都利用了图像覆盖的各类信息，如光谱信息、纹理信息、不同时相和不同传感器的信息等。在诸多分割方法中，二值化方法是一种特殊的图像分割技术，目的是将图像中的目标和背景部分用两种对立的颜色加以标识，以对图像中的目标进行初步判别，为后续的目标特征提取、场景分析等步骤提供基础。其中，应用最广泛的二值化方法是阈值分割法，该类算法给定一个灰度阈值，以此为门限对所有像素实施类别划分。Image segmentation method is an important means of image processing. Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. The correctness of the segmentation results directly affects target detection and The degree of intelligence of recognition. Image segmentation methods have experienced decades of development, and a large number of different types of algorithms have emerged. At present, image segmentation methods can be roughly divided into several categories: threshold-based methods, region-based methods, edge-based methods, region-edge-combined methods, and multi-scale segmentation methods. In addition, neural network, fuzzy mathematics, mathematical morphology and other theories have been widely used in the field of image segmentation in recent years, and many new algorithms have emerged. Although the principles of these algorithms are different, they basically use all kinds of information covered by the image, such as spectral information, texture information, information of different time phases and different sensors, etc. Among many segmentation methods, the binarization method is a special image segmentation technology, the purpose is to mark the target and the background part of the image with two opposite colors, so as to make a preliminary judgment on the target in the image, and provide a basis for the follow-up. The steps of target feature extraction and scene analysis provide the basis. Among them, the most widely used binarization method is the threshold segmentation method. This type of algorithm is given a gray threshold and uses this as the threshold to classify all pixels.
阈值分割法通常分为两类：全局法和局部法。全局法确定整张影像的单一阈值，并将其与影像各像素灰度值比较，以此分离目标和背景，该法简单且容易实现。局部法将整张影像分成若干具有一定尺寸的子图像，通过确定不同子图像的阈值将单个子图像逐一分类，最终再将子图像分割结果进行拼接，该法可以解决光照不均影响二值化分割结果的问题。总体而言，全局法和局部法存在以下问题：Threshold segmentation methods are generally divided into two categories: global methods and local methods. The global method determines a single threshold of the entire image and compares it with the gray value of each pixel of the image to separate the target from the background. This method is simple and easy to implement. The local method divides the entire image into several sub-images with a certain size, and classifies the individual sub-images one by one by determining the threshold of different sub-images, and finally splicing the sub-image segmentation results. This method can solve the problem of uneven illumination affecting binarization Problems with splitting results. In general, the global method and the local method have the following problems:
(1)全局阈值法(如Otsu法)只适合于图像的灰度直方图具有理想的双峰形状，当图像的灰度直方图呈现单峰或者多峰时则效果不甚理想，其分割结果依赖于均匀的光照条件；(1) The global threshold method (such as the Otsu method) is only suitable for the gray histogram of the image with an ideal bimodal shape. When the gray histogram of the image appears unimodal or multimodal, the effect is not ideal, and the segmentation results Depends on uniform lighting conditions;
(2)局部阈值法(如Niblack法)中的子图像尺寸选择主要采用经验法与试错法，即人为经验性地选取多组子图像尺寸对遥感影像进行二值化分割，最后选择分割结果较为理想的一组。该方法没有考虑影像的整体特征，且实施起来效率很低，主观性很大；(2) The sub-image size selection in the local threshold method (such as the Niblack method) mainly adopts the empirical method and the trial-and-error method, that is, artificially select multiple groups of sub-image sizes to perform binary segmentation on the remote sensing image, and finally select the segmentation result An ideal group. This method does not consider the overall characteristics of the image, and its implementation is very inefficient and highly subjective;
(3)局部阈值法通过将整张影像分解为许多子图像，并采用局部阈值对每张子图像单独进行二值化处理，最后将各子图像分割结果进行拼接。拼接过程中，各子图像在边缘连接处往往会产生分割结果差距较大的现象，导致明显的边界效应存在。(3) The local threshold method decomposes the entire image into many sub-images, and uses the local threshold to perform binarization processing on each sub-image, and finally stitches the segmentation results of each sub-image. During the splicing process, the sub-images often have a large gap in the segmentation results at the edge connection, resulting in the existence of obvious boundary effects.
由于以上存在的问题，现有图像二值化方法给影像自动解译带来很大的难度，极易导致目标被误判、漏判。因此，传统的图像阈值分割方法在应用于光谱信息丰富，观测范围大的影像目标识别时必须被改进。Due to the above problems, the existing image binarization method brings great difficulty to the automatic interpretation of the image, which can easily lead to misjudgment or missed judgment of the target. Therefore, the traditional image threshold segmentation method must be improved when it is applied to image target recognition with rich spectral information and large observation range.
发明内容Contents of the invention
本发明的目的在于提供一种在大区域、不均匀光照条件下影像识别效果较好，而且方法客观科学的图像二值化分割方法。The purpose of the present invention is to provide an image binarization segmentation method with better image recognition effect under large area and uneven illumination conditions, and the method is objective and scientific.
本发明提供的这种图像二值化分割方法，包括如下步骤：This image binarization segmentation method provided by the present invention comprises the following steps:
S1.获取目标图像；S1. Obtain the target image;
S2.将目标图像分割为若干张子图像；S2. Divide the target image into several sub-images;
S3.计算步骤S2得到的各子图像的均值、方差和灰度值；S3. Calculate the mean value, variance and gray value of each sub-image obtained in step S2;
S4.根据步骤S3得到的各子图像的均值和方差计算各子图像二值化分割的最佳阈值；S4. Calculate the optimal threshold value of each sub-image binary segmentation according to the mean value and variance of each sub-image obtained in step S3;
S5.将步骤S3得到的各子图像的灰度值与步骤S4得到的最佳阈值进行比较：将灰度值大于或等于最佳阈值的子图像中的所有像素点均标记为目标图像，同时将灰度值小于最佳阈值的子图像中的所有像素点均标记为背景图像；S5. The gray value of each sub-image obtained in step S3 is compared with the optimal threshold obtained in step S4: all pixels in the sub-image whose gray value is greater than or equal to the optimal threshold are marked as target images, and at the same time Mark all pixels in the sub-image whose gray value is less than the optimal threshold as the background image;
S6.重复步骤S2～S5共(MCS-1)次，计算目标图像中各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率；S6. Steps S2～S5 are repeated (MCS-1) times in total, and the probability that each pixel in the target image is marked as the target image or the probability that each pixel is marked as the background image is calculated;
S7.将步骤S6得到的各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率与事先设定的门限值进行对比，从而将目标图像中的各个像素点标记为目标图像或背景图像，从而完成目标图像的二值化分割。S7. Compare the probability that each pixel point obtained in step S6 is marked as the target image or the probability that each pixel point is marked as the background image with the threshold value set in advance, thereby marking each pixel point in the target image as Target image or background image, so as to complete the binary segmentation of the target image.
步骤S2所述的将目标图像分割为若干张子图像，具体为采用如下步骤进行分割：The target image described in step S2 is divided into several sub-images, specifically, the following steps are used for segmentation:
A.对于大小为M×N的目标图像，设定初始的窗口尺寸为num1；A. For the target image whose size is M×N, set the initial window size to be num1 ;
B.以num1×num1作为正方形窗口，将M×N的目标图像划分为若干子图像；B. With num1 * num1 as a square window, the target image of M * N is divided into several sub-images;
C.将步骤B中未划入num1×num1正方形窗口的像素点划入若干矩形窗口的子图像，从而将M×N的目标图像中的所有像素点均划分至各子图像中。C. Divide the pixels not included in the num1 ×num1 square window in step B into sub-images of several rectangular windows, so as to divide all the pixels in the M×N target image into each sub-image.
步骤C所述的矩形窗口，具体包括如下三种矩形窗口：The rectangular window described in step C specifically includes the following three rectangular windows:
第一矩形窗口：num1×num1'；The first rectangular window: num1 × num1 ';
第二矩形窗口：num1×num1”；The second rectangular window: num1 × num1 ";
第三矩形窗口：num1'×num1”；The third rectangular window: num1 '×num1 ";
其中且为向下取整符号。in and is the rounding down symbol.
步骤S4所述的计算二值化分割的最佳阈值，具体为采用Otsu算法计算最佳阈值。The calculation of the optimal threshold for binarization segmentation described in step S4 is specifically calculating the optimal threshold using the Otsu algorithm.
步骤S6所述的重复步骤S2～S5，具体为采用如下步骤进行重复：The steps S2-S5 described in step S6 are repeated, specifically, the following steps are used to repeat:
(1)对于第i次重复，重新设置窗口尺寸为numi，numi为小于M且小于N的自然数，且numi与之前的num1，num2，...，numi-1均不相等，并计算窗口尺寸numi'和numi”；i为大于1的自然数；(1) For the i-th repetition, reset the window size to numi , numi is a natural number less than M and less than N, and numi is different from the previous num1 , num2 , ..., numi-1 Equal, and calculate the window size numi 'and numi "; i is a natural number greater than 1;
(2)根据步骤(1)设置的窗口尺寸，将大小为M×N的目标图像划分为若干子图像；(2) according to the window size that step (1) is provided with, the target image that size is M * N is divided into several sub-images;
(3)计算步骤(2)得到的各子图像的均值、方差和灰度值；(3) mean value, variance and gray value of each sub-image that calculation step (2) obtains;
(4)根据步骤(3)得到的各子图像的均值和方差计算各子图像的二值化分割的最佳阈值；(4) calculate the optimal threshold of the binarization segmentation of each sub-image according to the mean value and the variance of each sub-image obtained in step (3);
(5)将步骤(3)得到的各子图像的灰度值与步骤(4)得到的最佳阈值进行比较：将灰度值大于或等于最佳阈值的子图像中的所有像素点均标记为目标图像，同时将灰度值小于最佳阈值的子图像中的所有像素点均标记为背景图像。(5) Compare the gray value of each sub-image obtained in step (3) with the optimal threshold obtained in step (4): mark all pixels in the sub-image whose gray value is greater than or equal to the optimal threshold is the target image, and all pixels in the sub-image whose gray value is smaller than the optimal threshold are marked as the background image.
步骤S6所述的计算目标图像中各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率，具体为采用如下算式进行概率计算：The calculation of the probability of each pixel in the target image being marked as the target image or the probability of each pixel being marked as the background image described in step S6 is specifically to use the following formula to calculate the probability:
式中Pi为第i个像素点被标记为目标图像的概率或被标记为背景图像的概率；MCS为重复的次数；Ppi为第i个像素点在MCS次重复的过程中被标记为目标图像的次数或在MCS次重复的过程中被标记为背景图像的次数。In the formula, Pi is the probability that the i-th pixel is marked as the target image or the probability of being marked as the background image; MCS is the number of repetitions; Ppi is the i-th pixel is marked as The number of times the target image or the number of times it was marked as a background image during MCS repetitions.
步骤S7所述的将目标图像中的各个像素点标记为目标图像或背景图像，具体为采用如下规则进行标记：若第i个像素点被标记为目标图像的概率大于事先设定的门限阈值，则将该像素点标记为目标图像，否则将该像素点标记为背景图像。Marking each pixel in the target image as a target image or a background image as described in step S7 specifically uses the following rules for marking: if the probability that the i-th pixel is marked as a target image is greater than a preset threshold, Then mark the pixel as the target image, otherwise mark the pixel as the background image.
本发明提供的这种图像二值化分割方法，自动地对区域性复杂图像进行最优二值化分割，算法简单方便，不仅克服了应用全局阈值图像分割方法不能处理不均匀光照条件问题，也解决了已有局部阈值分割方法中人为确定分割尺度的主观性及在子图像合并时出现的锯齿效应问题，通过标记子图象的二值化分割结果，最终还原为原始图像的目标和背景的分类图，能够克服传统图像分割算法在应用于大区域不均匀光照条件下的缺陷问题。The image binarization segmentation method provided by the present invention automatically performs optimal binarization segmentation on regional complex images. It solves the subjectivity of artificially determining the segmentation scale in the existing local threshold segmentation method and the problem of sawtooth effect when the sub-images are merged. By marking the binary segmentation results of the sub-images, it is finally restored to the target and background of the original image. The classification map can overcome the defects of traditional image segmentation algorithms when applied to large areas with uneven lighting conditions.
附图说明Description of drawings
图1为本发明方法的方法流程图。Fig. 1 is the method flowchart of the method of the present invention.
图2为第i次将目标图像分割为子图像时的分割示意图。FIG. 2 is a schematic diagram of segmentation when the target image is segmented into sub-images for the i-th time.
图3为本发明方法与传统方法在某具体图像二值化分割时的结果示意图。Fig. 3 is a schematic diagram of the results of the method of the present invention and the traditional method in the binarization and segmentation of a specific image.
具体实施方式Detailed ways
如图1所示为本发明方法的方法流程图：本发明提供的这种图像二值化分割方法，包括如下步骤：As shown in Figure 1, it is a method flowchart of the method of the present invention: this image binarization segmentation method provided by the present invention comprises the following steps:
S1.获取目标图像；S1. Obtain the target image;
S2.采用如下步骤将目标图像分割为若干张子图像；S2. Using the following steps to divide the target image into several sub-images;
A.对于大小为M×N的目标图像，设定初始的窗口尺寸为num1；A. For the target image whose size is M×N, set the initial window size to be num1 ;
B.以num1×num1作为正方形窗口，将M×N的目标图像划分为若干子图像；B. With num1 * num1 as a square window, the target image of M * N is divided into several sub-images;
C.将步骤B中未划入num1×num1正方形窗口的像素点划入若干矩形窗口的子图像，从而将M×N的目标图像中的所有像素点均划分至各子图像中；C. divide the pixel points that are not divided into num1 * num1 square windows into the sub-images of several rectangular windows in step B, thereby all the pixels in the target image of M * N are divided into each sub-image;
在具体实施时，矩形窗口可以采用如下三种矩形窗口：In specific implementation, the rectangular window can adopt the following three types of rectangular windows:
第一矩形窗口：num1×num1'；The first rectangular window: num1 × num1 ';
第二矩形窗口：num1×num1”；The second rectangular window: num1 × num1 ";
第三矩形窗口：num1'×num1”；The third rectangular window: num1 '×num1 ";
其中且为向下取整符号；in and is the rounding down symbol;
S3.计算步骤S2得到的各子图像的均值、方差和灰度值；S3. Calculate the mean value, variance and gray value of each sub-image obtained in step S2;
S4.根据步骤S3得到的各子图像的均值和方差，采用Otsu算法计算二值化分割的最佳阈值；S4. According to the mean value and the variance of each sub-image obtained in step S3, adopt the Otsu algorithm to calculate the optimum threshold of binarized segmentation;
S5.将步骤S3得到的各子图像的灰度值与步骤S4得到的最佳阈值进行比较：将灰度值大于或等于最佳阈值的子图像中的所有像素点均标记为目标图像，同时将灰度值小于最佳阈值的子图像中的所有像素点均标记为背景图像；S5. The gray value of each sub-image obtained in step S3 is compared with the optimal threshold obtained in step S4: all pixels in the sub-image whose gray value is greater than or equal to the optimal threshold are marked as target images, and at the same time Mark all pixels in the sub-image whose gray value is less than the optimal threshold as the background image;
S6.重复步骤S2～S5共(MCS-1)次，计算目标图像中各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率；S6. Steps S2～S5 are repeated (MCS-1) times in total, and the probability that each pixel in the target image is marked as the target image or the probability that each pixel is marked as the background image is calculated;
在具体重复步骤S2～S5时，具体为采用如下步骤进行重复：When specifically repeating steps S2-S5, specifically, the following steps are used to repeat:
(1)对于第i次重复，重新设置窗口尺寸为numi，numi为小于M且小于N的自然数，且numi与之前的num1，num2，...，numi-1均不相等，并计算窗口尺寸numi'和numi”；i为大于1的自然数；(1) For the i-th repetition, reset the window size to numi , numi is a natural number less than M and less than N, and numi is different from the previous num1 , num2 , ..., numi-1 Equal, and calculate the window size numi 'and numi "; i is a natural number greater than 1;
(2)根据步骤(1)设置的窗口尺寸，将大小为M×N的目标图像划分为若干子图像(如图2所示)；(2) according to the window size that step (1) is provided with, the target image that size is M * N is divided into several sub-images (as shown in Figure 2);
(3)计算步骤(2)得到的各子图像的均值、方差和灰度值；(3) mean value, variance and gray value of each sub-image that calculation step (2) obtains;
(4)根据步骤(3)得到的各子图像的均值和方差计算二值化分割的最佳阈值；(4) mean value and variance of each sub-image obtained according to step (3) calculate the optimal threshold of binarized segmentation;
(5)将步骤(3)得到的各子图像的灰度值与步骤(4)得到的最佳阈值进行比较：将灰度值大于或等于最佳阈值的子图像中的所有像素点均标记为目标图像，同时将灰度值小于最佳阈值的子图像中的所有像素点均标记为背景图像；(5) Compare the gray value of each sub-image obtained in step (3) with the optimal threshold obtained in step (4): mark all pixels in the sub-image whose gray value is greater than or equal to the optimal threshold is the target image, and all pixels in the sub-image whose gray value is less than the optimal threshold are marked as the background image;
同时，采用如下算式计算目标图像中各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率：At the same time, the probability that each pixel in the target image is marked as the target image or the probability that each pixel is marked as the background image is calculated using the following formula:
式中Pi为第i个像素点被标记为目标图像的概率或被标记为背景图像的概率；MCS为重复的次数；Ppi为第i个像素点在MCS次重复的过程中被标记为目标图像的次数或在MCS次重复的过程中被标记为背景图像的次数；In the formula, Pi is the probability that the i-th pixel is marked as the target image or the probability of being marked as the background image; MCS is the number of repetitions; Ppi is the i-th pixel is marked as The number of times the target image or the number of times it was marked as a background image during MCS repetitions;
S7.将步骤S6得到的各个像素点被标记为目标图像的概率或各个像素点被标记为背景图像的概率与事先设定的门限值进行对比，按照如下规则将目标图像中的各个像素点标记为目标图像或背景图像：S7. The probability that each pixel obtained in step S6 is marked as the target image or the probability that each pixel is marked as the background image is compared with the threshold value set in advance, and each pixel in the target image is compared according to the following rules Mark as target image or background image:
若第i个像素点被标记为目标图像的概率大于事先设定的门限阈值，则将该像素点标记为目标图像，否则将该像素点标记为背景图像；If the probability that the i-th pixel is marked as the target image is greater than the threshold threshold set in advance, then mark the pixel as the target image, otherwise mark the pixel as the background image;
在各个像素点均被标记完成后，目标图像的二值化分割完成。After each pixel point is marked, the binarization segmentation of the target image is completed.
如图3所示为本发明方法与传统方法在某具体图像的二值化分割时的结果示意图：图3(a)为图像识别领域中经典的车牌识别图像的目标图像(原始图像)，因其尾灯开启导致整副图像的光谱差异很大，造成很多传统方法应用于该影像识别时出现误差。如图3(b)为直接用Otsu方法对该目标图像进行二值化分割的结果，图3(c)为Niblack法将窗口尺寸设置为15时对该目标图像进行二值化分割的结果，图3(d)为Niblack法将窗口尺寸设置为50时对该目标图像进行二值化分割的结果，图3(e)为应用本发明方法对该目标图像进行二值化分割所得到的概率图，图3(f)为本发明方法对该目标图像进行二值化分割的最终结果。由此看出，直接应用Otsu方法对车牌识别的结果非常不理想；应用Niblack方法进行图像分割，其分割结果对窗口尺寸的设置非常敏感，在具体应用时该方法难度较大；本发明提出的方法只需设置一个初始的窗口尺寸，即可得到满意的二值化结果。As shown in Figure 3, it is a schematic diagram of the results of the method of the present invention and the traditional method in the binary segmentation of a specific image: Figure 3 (a) is the target image (original image) of a classic license plate recognition image in the field of image recognition, because Turning on the taillights leads to a large difference in the spectrum of the entire image, causing errors when many traditional methods are applied to the image recognition. Figure 3(b) is the result of binary segmentation of the target image directly using the Otsu method, and Figure 3(c) is the result of binary segmentation of the target image when the window size is set to 15 by the Niblack method. Figure 3(d) is the result of binary segmentation of the target image when the window size is set to 50 by the Niblack method, and Figure 3(e) is the probability obtained by applying the method of the present invention to the binary segmentation of the target image Fig. 3(f) is the final result of binary segmentation of the target image by the method of the present invention. From this it can be seen that the result of directly applying the Otsu method to license plate recognition is very unsatisfactory; the Niblack method is used to carry out image segmentation, and its segmentation result is very sensitive to the setting of the window size, and this method is difficult in specific applications; the proposed method of the present invention The method only needs to set an initial window size, and a satisfactory binarization result can be obtained.
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| CN201711339682.9ACN107945200B (en) | 2017-12-14 | 2017-12-14 | Image Binarization Segmentation Method |
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| CN201711339682.9ACN107945200B (en) | 2017-12-14 | 2017-12-14 | Image Binarization Segmentation Method |
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| CN107945200Atrue CN107945200A (en) | 2018-04-20 |
| CN107945200B CN107945200B (en) | 2021-08-03 |
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| CN201711339682.9AActiveCN107945200B (en) | 2017-12-14 | 2017-12-14 | Image Binarization Segmentation Method |
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